1. Technical Field
The present disclosure relates to image processing, and more particularly to a system and method for automatically updating a geometric model of anatomy or function using actively requested acquisition data from an imaging device.
2. Description of Related Art
Magnetic Resonance Imaging (MRI) has become one of the most important imaging modalities for medial diagnostics. Although the diagnoses are still often based upon human inspection of the acquired image material, the development of sophisticated image post-processing and analysis methods can greatly support physicians in their decisions for treatment. Combining image analysis results with physics-based prior knowledge leads to mathematical models of anatomy and/or function, which can be used to visualize structures and processes inside the body, like the beating human heart. Furthermore, quantitative measure can be derived from the model, that could otherwise not be measured non-invasively, e.g., ejection fraction, which is an important indicator of the heart's efficiency.
Moving from traditional diagnostic (offline) imaging to MRI guided intervention, scanning can be performed continuously and the model of choice can be updated directly with the gathered data. Models relevant in this scenario can be topological maps to guide the operator, but also models that provide quantitative data for monitoring the progress of the intervention.
In currently available interventional MRI (iMRI) systems, guidance is either purely image-based or using a geometrical model retrieved semi-automatically from a pre-acquired dataset and kept static throughout the process. In many real-time scenarios however, a dynamic representation of the anatomy is required that constantly adapts to changes of position and shape over time. One application example is an interventional procedure, where a graphical visualization of the model helps the physician to drive a catheter. Here the model has to reflect the current form and state of the organ(s) of interest in the best way possible. Depending on the application, the optimal tradeoff between spatial and temporal detail can vary.
In a simple case, the complete set of relevant image data is acquired over and over, each time fitting the anatomic model to the most recent data. Although this can be a viable solution if the necessary amount of data can be acquired at a reasonable frequency, in most practical scenarios a higher update rate is desirable. In many applications, only a certain subset of model parameters requires a frequent update. In the example of a 3D model used for navigation by the operator, the local region around the catheter tip is of most interest, and thus needs most frequent updates. If the parameter subset can be mapped to a certain locality of the input data, the updating procedure can be accelerated by repeatedly acquiring only this region.
In image guided procedures, “roadmap” images are used to enable visualization of devices used in the procedure, and to allow the operator to avoid critical structures. Roadmap images typically are acquired prior to a procedure and give an overview of the anatomy and/or function targeted for the procedure. Ideally one would like to have these roadmap images updated continuously, but limitations in acquisition speed, radiation exposure, procedure time, etc. limit the rate at which one can update the images.
In current image guided systems, the user must acquire an entirely new roadmap image or set of images. There are methods to represent the age of roadmap images during x-ray guided procedures, slowly fading out the roadmap image at a preset rate to give the operator a visual clue that the roadmap is outdated. The operator is thus triggered to acquire a new roadmap image. The disadvantage of the current systems is that they exist only for fluoroscopy, and only in the simple method described above. No automated update strategies for MR (Magnetic Resonance) guided interventions currently exist.
Therefore, a need exists for a system and method for automatically updating a geometric model.